{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. \n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from DSVC.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'DSVC/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **DSVC/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 8.716261\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from DSVC.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 4.682776 analytic: 4.682776, relative error: 5.356790e-11\n",
      "numerical: 10.288915 analytic: 10.288915, relative error: 5.967393e-12\n",
      "numerical: 18.775795 analytic: 18.775795, relative error: 2.950188e-12\n",
      "numerical: -2.230878 analytic: -2.230878, relative error: 1.012045e-10\n",
      "numerical: 4.238074 analytic: 4.238074, relative error: 2.969153e-11\n",
      "numerical: 6.298684 analytic: 6.298684, relative error: 2.068450e-11\n",
      "numerical: -16.644737 analytic: -16.644737, relative error: 7.010237e-12\n",
      "numerical: -11.191047 analytic: -11.191047, relative error: 1.756897e-11\n",
      "numerical: 18.805274 analytic: 18.805274, relative error: 1.265120e-11\n",
      "numerical: 9.240784 analytic: 9.240784, relative error: 1.374532e-11\n",
      "numerical: -25.841204 analytic: -25.833725, relative error: 1.447315e-04\n",
      "numerical: 9.932683 analytic: 9.938739, relative error: 3.047804e-04\n",
      "numerical: 5.066315 analytic: 5.061720, relative error: 4.537650e-04\n",
      "numerical: -9.988857 analytic: -9.987834, relative error: 5.120187e-05\n",
      "numerical: -10.291240 analytic: -10.291535, relative error: 1.431805e-05\n",
      "numerical: 11.126144 analytic: 11.121395, relative error: 2.134615e-04\n",
      "numerical: -12.671643 analytic: -12.668659, relative error: 1.177441e-04\n",
      "numerical: -46.389604 analytic: -46.394991, relative error: 5.806268e-05\n",
      "numerical: 21.063891 analytic: 21.057581, relative error: 1.498173e-04\n",
      "numerical: -15.690838 analytic: -15.690589, relative error: 7.915547e-06\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from DSVC.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline Question 1:\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "**Your Answer:** *fill this in.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 8.716261e+00 computed in 0.284075s\n",
      "Vectorized loss: 8.716261e+00 computed in 0.106025s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from DSVC.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.250065s\n",
      "Vectorized loss and gradient: computed in 0.017009s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 396.499460\n",
      "iteration 100 / 1500: loss 234.664841\n",
      "iteration 200 / 1500: loss 143.671493\n",
      "iteration 300 / 1500: loss 86.784173\n",
      "iteration 400 / 1500: loss 55.604571\n",
      "iteration 500 / 1500: loss 35.202020\n",
      "iteration 600 / 1500: loss 23.379252\n",
      "iteration 700 / 1500: loss 16.096966\n",
      "iteration 800 / 1500: loss 11.668675\n",
      "iteration 900 / 1500: loss 9.428029\n",
      "iteration 1000 / 1500: loss 6.829485\n",
      "iteration 1100 / 1500: loss 6.438288\n",
      "iteration 1200 / 1500: loss 5.908006\n",
      "iteration 1300 / 1500: loss 5.631816\n",
      "iteration 1400 / 1500: loss 5.254160\n",
      "That took 37.390256s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from DSVC.classifiers import linear_classifier\n",
    "svm = linear_classifier.LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.380980\n",
      "validation accuracy: 0.375000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\DSVC\\assignment4\\homework\\DSVC\\classifiers\\linear_svm.py:90: RuntimeWarning: overflow encountered in double_scalars\n",
      "  loss += 0.5 * reg * np.sum(W * W)\n",
      "D:\\Anaconda\\lib\\site-packages\\numpy\\core\\_methods.py:32: RuntimeWarning: overflow encountered in reduce\n",
      "  return umr_sum(a, axis, dtype, out, keepdims)\n",
      "E:\\DSVC\\assignment4\\homework\\DSVC\\classifiers\\linear_svm.py:90: RuntimeWarning: overflow encountered in multiply\n",
      "  loss += 0.5 * reg * np.sum(W * W)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-07 reg 2.500000e+04 train accuracy: 0.378653 val accuracy: 0.378000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.371082 val accuracy: 0.380000\n",
      "lr 5.000000e-05 reg 2.500000e+04 train accuracy: 0.126347 val accuracy: 0.137000\n",
      "lr 5.000000e-05 reg 5.000000e+04 train accuracy: 0.058980 val accuracy: 0.063000\n",
      "best validation accuracy achieved during cross-validation: 0.380000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.4 on the validation set.\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "for rate in learning_rates:\n",
    "    for regular in regularization_strengths:\n",
    "        svm = linear_classifier.LinearSVM()\n",
    "        svm.train(X_train, y_train, learning_rate=rate, reg=regular,\n",
    "                      num_iters=1000)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        accuracy_train = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        accuracy_val = np.mean(y_val == y_val_pred)\n",
    "        results[(rate, regular)]=(accuracy_train, accuracy_val)\n",
    "        if (best_val < accuracy_val):\n",
    "            best_val = accuracy_val\n",
    "            best_svm = svm\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "train_acc,val_acc = results[(lr,reg)]\n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.374000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 2:\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "**Your answer:权重是对原图像的特征进行提取，训练样本的得出的权重的最好结果就是与提取出的特征相似。"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
